1. Statement of the Technical Field
The invention concerns computing systems. More particularly, the invention concerns computing systems and methods for efficient feature based image and video analysis.
2. Description of the Related Art
There are many known imagery systems that collect and maintain spatial data. Such imagery systems include satellite based systems which collect imagery data covering particular areas of the Earth. The imagery data is typically analyzed for a variety of reasons, such as for surveillance purposes, quality control purposes and/or change detection purposes. The analysis often involves manually analyzing the imagery data over an area of interest. Such manual analysis is often achieved using a computer executing image analysis software (e.g., ESRI® ArcMap® Geospatial Information System (“GIS”) software, SOCET SET® software, FALCONVIEW® software, ADOBE® PHOTOSHOP® software, computer aided design software, and computer aided manufacturing software). In this scenario, only a portion of a high resolution image may be displayed to an operator at any given time. As such, the software provides a pan function and a zoom function. The pan function allows the operator to change a viewport from one part of an image to another part of the image. The zoom function allows the operator to change from a distant view of an image to a more close-up view (zoom in) of the image, and vice versa (zoom out). The pan and zoom operations are typically enabled by the operator using a computer mouse, joy stick and/or gestures.
During the image analysis process, the operator manually inspects the area of interest by: (a) obtaining feature data specifying locations and characteristics of a plurality of objects (e.g., gas stations); (b) “panning” to an area of interest within an image that is supposed to include a visual representation of at least one of the objects; (c) “zooming in” to obtain a close-up view of the area of interest; (d) visually comparing the current image to the feature data to determine if the object is still present in the area of interest and/or has the characteristics defined by the feature data; and (e) repeating steps (a)-(d) for each of the objects indentified by the feature data. Notably, one or more “panning” and/or “zooming” operations may need to be performed to obtain a desired view of the area of interest. Such a manual inspection is time consuming, costly and subject to human error.
Additionally, the large amount of video surveillance data collected and maintained today requires increasingly efficient methods for analysis. There are many challenges to the analysis of video which are imposed by its usage as a forensic tool across military applications, law enforcement applications and commercial applications. For example, video analysis is used in unmanned mission applications, critical transportation surveillance applications, energy infrastructure surveillance applications, online geospatial video portal applications, medical applications and industrial production applications. These applications share a common need for efficient analysis of video data which may or may not exist within a geospatial context.
Some traditional approaches for video analysis involve manual video play-back and/or frame-by-frame analysis. One can readily appreciate that techniques employing manual video play-back and frame-by-frame analysis are ad-hoc, time consuming and expensive. Other traditional approaches for video analysis involve comparing the content of two or more video streams. This comparison is achieved by toggling between different video streams or by viewing different video streams that are presented in a side by side manner. Such comparison techniques are time consuming and subject to human error as a result of operator fatigue.